Ursula Gonzales-Barron’s Post

AgroStat2024 will host the workshop 💡"Structural equation modelling from covariance analysis to PLS-SEM", delivered by 🎤 Jean Michel Galharet, ONIRIS, Nantes. 🙇♂️ 🙇♀️ In these models, an unobserved variable (latent variable) is associated with each block of matched data and we are interested in the set of regression equations linking these variables together. The coefficients of these models can be estimated using analysis of covariances or the PLS approach. 👉⌛To attend this workshop to learn about the principles of PLS-SEM and the R package lavaan, register at: https://lnkd.in/eWbeGRFj.

[PDF] Solution of logistic differential equation in an uncertain environment using neutrosophic numbers M Parikh, M Sahni The modeling and forecasting of population dynamics, as well as growth in biological systems more generally, have required the construction of various growth models. This paper presents the logistic growth model, which is a modified version …

Getting stuck on an SAT Math question? Ask yourself: Can I just use Desmos? Many questions, especially those that include equations, can be solved by using the Desmos calculator. If you’re stuck, stop and check to see if Desmos can make the problem easier! For more tips and tricks for the SAT Math section, visit our blog: https://loom.ly/XtHHvM4 #SAT #Math #SATTest #TestPrep

Good evening everyone, I am reproposing a previously shared article on the generalized inhomogeneous Burgers equation. I have revised the form and notation and corrected some errors in the calculations. Here I also post the abstract and the introduction to the article, waiting to send it to some platform and also to some journal for peer review.  In this article we study generalizations of the inhomogeneous Burgers equation. First at the operator level, in the sense that we replace classical differential derivations by operators with certain properties, and then we increase the spatial dimensions of the Burgers equation, which is usually studied in one spatial dimension. This allows us, in one dimension, to find mathematical relationships between solutions of hyperbolic Brownian motion and the Burgers equations, which usually study the behaviour of mechanical fluids, and also, through appropriate transformations, to obtain in some cases exact solutions that depend on Hermite polynomials composed of appropriate functions. In the multi-dimensional case, this generalization allows us, by means of the method of invariant spaces, to find exact solutions on Riemannian and pseudo-Riemannian varieties, such as Schwarzschild and Ricci Solitons space, with time dictated by fractional derivatives, such as a Caputo-type operator of fractional evolution.

When you refer to the Central Limit Theorem do always specify which one you mean: ➡️ Lindeberg-Levy the oldest one; needs IID variables and finite mean and variance. In other words - it's quite limited (independently and same distributed random variables) but has simple assumptions offering faster convergence in distribution - no free lunch 🤷♂️ ➡️ Lyapunov relaxes the need for same distributions, but has strong assumption: finite first 2 moments and sufficiently fast vanishing higher moments as N grows (meaning "not to heavy tails" ) - no free lunch 🤷♂️ ➡️ Lindeberg as Lyapunov, relaxes the need for identically distributed variables, but has a stronger condition, that no individual variable dominates the sum. Or differently - that contributions to the total variance from variables showing large deviations from their means variable vanish as N grows. BTW, if Lindeberg condition holds, then Lyapunov one holds too. ➡️ Gibrat's law aka Multiplicative CLT for IID log-normally distributed variables. Naturally uses the product rather than sums, leading to the log-normal distribution. Or the normal one for log(sum). ➡️ Generalized Kolmogorov-Gniedenko LT about converging to alpha-stable distributions (Gaussian is a special case, but not the main target here!) for IID variables but do NOT require finite variance and allows for heavy-tailed ones (Levy stable ones). at least as far as I recall... ➡️ CLTs mentioned in the Wither's and Orey's papers - allowing for dependent variables. e.g. read https://lnkd.in/dBHdEbJU Also, check out this interesting article. https://lnkd.in/di44fQpe #statistics #datascience #clt

can we able to create indexes on derived columns?

Good and concise summary.

#Vortex - indicator MetaTrader 5 https://lnkd.in/eWen5rk2 # According to original description of the authors (Etienne Botes and Douglas Siepman) of the Vortex indicator: After thoroughly researching technical tools, we concluded that the concept of the Directional Movement Index (DMI) offered the most accurate way to ide...

Multi-disciplinary approach research is the best tool required to solve water and environmental problems. There is always something to learn from colleagues with complimentary skills. Our paper merged machine learning, statistics, remote sensing technology, GIS and modelling for water quality monitoring and management.

Why have data-driven FWI methods not made significant progress in the recent years? We addressed this question in our latest publication, "Generalizable Data-Driven Full Waveform Inversion for Complex Structures and Severe Topographies," published in the journal Petroleum Science. In this paper, Dr.Hosein Hashemi, Dr.Majid Nabi-Bidhendi , and I had an in-depth discussion on how deep networks memorize common features of the training data and when they start to overfit. We also presented a solution to this problem by incorporating the acquisition geometry into the input data and manipulating the loss function. https://lnkd.in/dkB7VqTM https://lnkd.in/db4TpXXv #deep_learning #data_driven #FWI